def UpdateSummary(self, values):
try:
self.numOfPanelCtrl.SetValue(values[0])
except:
pass
try:
self.widthCtrl.SetValue(str(round(float(values[1]), 3)))
except:
pass
try:
self.areaCtrl.SetValue(sigfig.round_sig(float(values[2]), 3))
except:
pass
try:
self.avgDepthCtrl.SetValue(str(round(float(values[3]), 3)))
except:
pass
try:
self.avgVelCtrl.SetValue(sigfig.round_sig(float(values[4]), 3))
except:
pass
try:
self.totalDisCtrl.SetValue(sigfig.round_sig(float(values[5]), 3))
except:
pass
self.startTimeCtrl.SetValue(values[6])
self.endTimeCtrl.SetValue(values[7])
def posteriors_plot(self, traces, database, stats_dic):
#Number of traces to plot
n_traces = len(traces)
#Declare figure format
size_dict = {'figure.figsize':(14,20), 'axes.labelsize':20, 'legend.fontsize':10}
self.FigConf(plotSize = size_dict, Figtype = 'Grid', n_columns = 1, n_rows = n_traces)
#Generate the color map
self.gen_colorList(0, n_traces)
#Plot individual traces
for i in range(len(traces)):
#Current trace
trace_code = traces[i]
mean_value = stats_dic[trace_code]['mean']
trace_array = stats_dic[trace_code]['trace']
#Plot HDP limits
HDP_coords = stats_dic[trace_code]['95% HPD interval']
for HDP in HDP_coords:
if mean_value > 0.001:
label_limits = 'HPD interval: {} - {}'.format(round_sig(HDP_coords[0], 4), round_sig(HDP_coords[1], 4))
label_mean = 'Mean value: {}'.format(round_sig(mean_value, 4))
else:
label_limits = 'HPD interval: {:.3e} - {:.3e}'.format(HDP_coords[0], HDP_coords[1])
label_mean = 'Mean value: {:.3e}'.format(mean_value)
self.Axis[i].axvline(x = HDP, label = label_limits, color='grey', linestyle = 'dashed')
self.Axis[i].axvline(x = mean_value, label = label_mean, color='grey', linestyle = 'solid')
self.Axis[i].hist(trace_array, histtype='stepfilled', bins=35, alpha=.7, color=self.get_color(i), normed=False)
self.Axis[i].set_ylabel(self.labels_latex_dic[trace_code])
#Add legend
self.legend_conf(self.Axis[i], loc=2)
def print_data(self, fp):
fp.write("\\startdata\n")
for i, data in enumerate(self.data):
if self.data_labels[i] != '':
if self.data_label_types == "cutin":
fp.write("\\cutinhead{%s}\n" % self.data_labels[i])
else:
fp.write("\\sidehead{%s}\n" % self.data_labels[i])
rows = []
print 'data=', data
for j in range(numpy.shape(data[0])[0]):
rows.append([])
for k in range(len(data)):
sf = self.sigfigs[i][k]
if len(numpy.shape(data[k])) == 1:
if type(data[k][j]) in float_types:
if numpy.isnan(data[k][j]):
rows[-1].append('\\ldots')
else:
rows[-1].append(
sigfig.round_sig(data[k][j], sf))
else:
rows[-1].append(str(data[k][j]))
else:
print data[k]
if numpy.isnan(data[k][j, 0]):
val = "\\ldots"
else:
val = sigfig.round_sig_error(data[k][j, 0],
data[k][j, 1],
sf,
paren=True)
rows[-1].append(val)
for row in rows:
fp.write(" & ".join(row))
fp.write("\\\\\n")
fp.write("\\enddata\n")
def print_data(self,fp):
fp.write("\\startdata\n")
for i,data in enumerate(self.data):
if self.data_labels[i] != '':
if self.data_label_types == "cutin":
fp.write("\\cutinhead{%s}\n" % self.data_labels[i])
else:
fp.write("\\sidehead{%s}\n" % self.data_labels[i])
rows = []
for j in range(numpy.shape(data[0])[0]):
rows.append([])
for k in range(len(data)):
sf = self.sigfigs[i][k]
if len(numpy.shape(data[k])) == 1:
if type(data[k][j]) in float_types:
if numpy.isnan(data[k][j]):
rows[-1].append('\\ldots')
else:
rows[-1].append(sigfig.round_sig(data[k][j], sf))
else:
rows[-1].append(str(data[k][j]))
else:
print data[k]
print 'El cosos de error', [j,0]
# if numpy.isnan(data[k][j,0]):
if numpy.isnan(data[k][j][0]):
val = "\\ldots"
else:
val = sigfig.round_sig_error(data[k][j][0],data[k][j][1],sf,
paren=True)
rows[-1].append(val)
for row in rows:
fp.write(" & ".join(row))
fp.write("\\\\\n")
fp.write("\\enddata\n")
def add_row(self, row, sigfig, errorup=None, errordown=None, percent=False):
if sigfig is None:
temp = np.array(column).reshape((1, len(temp)))
else:
temp = []
for i, v in enumerate(row):
if (errordown is not None) and (errorup is not None):
rounded = sf.round_sig_error2(v, errorup[i], errordown[i], sigfig)
string = "$%s ^{+%s}_{-%s}" % rounded
elif (errordown is None) and (errorup is not None):
rounded = sf.round_sig_error(v, errorup[i], sigfig)
string = "$%s \\pm %s" % rounded
elif (errorup is None):
rounded = sf.round_sig(v, sigfig)
string = "$%s" % rounded
if percent:
string += "\\%$"
else: string += "$"
temp.append(string)
temp = np.array(temp).reshape((1, len(temp)))
if self.array is None:
self.array = deepcopy(temp)
else:
self.array = np.vstack((self.array, temp))
lamost_id = dat['LAMOST_ID'][choose] lamost_id = np.array([fmt_id(val) for val in lamost_id]) ra = dat['RA'][choose] dec = dat['Dec'][choose] teff = dat['Teff'][choose] logg = dat['logg'][choose] mh = dat['MH'][choose] cm = dat['CM'][choose] nm = dat['NM'][choose] am = dat['AM'][choose] ak = dat['Ak'][choose] mass = dat['Mass'][choose] logAge = dat['logAge'][choose] teff = np.array([int(val) for val in teff]) logg = np.array([round_sig(val, 3) for val in logg]) mh = np.array([round_sig(val, 3) for val in mh]) cm = np.array([round_sig(val, 3) for val in cm]) nm = np.array([round_sig(val, 3) for val in nm]) am = np.array([round_sig(val, 3) for val in am]) ak = np.array([round_sig(val, 3) for val in ak]) mass = np.array([round_sig(val, 2) for val in mass]) logAge = np.array([round_sig(val, 2) for val in logAge]) teff_err = dat['Teff_err'][choose] logg_err = dat['logg_err'][choose] mh_err = dat['MH_err'][choose] cm_err = dat['CM_err'][choose] nm_err = dat['NM_err'][choose] am_err = dat['AM_err'][choose] ak_err = dat['Ak_err'][choose]
def traces_plot(self, traces, database, stats_dic):
#Number of traces to plot
n_traces = len(traces)
#Declare figure format
size_dict = {'figure.figsize':(14,20), 'axes.labelsize':12, 'legend.fontsize':14}
self.FigConf(plotSize = size_dict, Figtype = 'Grid', n_columns = 1, n_rows = n_traces)
#Generate the color map
self.gen_colorList(0, n_traces)
#Plot individual traces
for i in range(n_traces):
#Current trace
trace_code = traces[i]
trace_array = stats_dic[trace_code]['trace']
#Label for the plot
mean_value = stats_dic[trace_code]['mean']
std_dev = stats_dic[trace_code]['standard deviation']
if mean_value > 0.001:
label = r'{} = ${}$ $\pm${}'.format(self.labels_latex_dic[trace_code], round_sig(mean_value, 4), round_sig(std_dev, 4))
else:
label = r'{} = ${:.3e}$ $\pm$ {:.3e}'.format(self.labels_latex_dic[trace_code], mean_value, std_dev)
#Plot the data
self.Axis[i].plot(trace_array, label=label, color = self.get_color(i))
self.Axis[i].axhline(y = mean_value, color=self.get_color(i), linestyle = '--' )
self.Axis[i].set_ylabel(self.labels_latex_dic[trace_code])
if i < n_traces - 1:
self.Axis[i].set_xticklabels([])
#Add legend
self.legend_conf(self.Axis[i], loc=2)
return
lamost_id = dat['LAMOST_ID'][choose] lamost_id = np.array([fmt_id(val) for val in lamost_id]) ra = dat['RA'][choose] dec = dat['Dec'][choose] teff = dat['Teff'][choose] logg = dat['logg'][choose] mh = dat['MH'][choose] cm = dat['CM'][choose] nm = dat['NM'][choose] am = dat['AM'][choose] ak = dat['Ak'][choose] mass = dat['Mass'][choose] logAge = dat['logAge'][choose] teff = np.array([int(val) for val in teff]) logg = np.array([round_sig(val,3) for val in logg]) mh = np.array([round_sig(val, 3) for val in mh]) cm = np.array([round_sig(val, 3) for val in cm]) nm = np.array([round_sig(val, 3) for val in nm]) am = np.array([round_sig(val, 3) for val in am]) ak = np.array([round_sig(val, 3) for val in ak]) mass = np.array([round_sig(val, 2) for val in mass]) logAge = np.array([round_sig(val, 2) for val in logAge]) teff_err = dat['Teff_err'][choose] logg_err = dat['logg_err'][choose] mh_err = dat['MH_err'][choose] cm_err = dat['CM_err'][choose] nm_err = dat['NM_err'][choose] am_err = dat['AM_err'][choose] ak_err = dat['Ak_err'][choose]
med_a = median(the_a)
med_b = median(the_b)
med_c = median(the_c)
med_R = median(the_R)
e_a = sqrt(covmat[0, 0])
e_b = sqrt(covmat[1, 1])
e_c = sqrt(covmat[2, 2])
e_R = sqrt(covmat[3, 3])
sig = sqrt(median(the_sig))
l = ['%s(%s)' % (data.filters[i], "-".join(cf.Data.color))]
l += list(sigfig.round_sig_error(med_a, e_a, 2))
l += list(sigfig.round_sig_error(med_b, e_b, 2))
l += list(sigfig.round_sig_error(med_c, e_c, 2))
l += list(sigfig.round_sig_error(med_R, e_R, 2))
l += [sigfig.round_sig(sig, 2)]
outcols.append(l)
outcols2 = [[sigfig.round_sig(covmat[i,j],2) for i in range(3)] \
for j in range(3)]
format = ""
for j in range(len(outcols[0])):
maxlen = max([len(outcols[i][j]) for i in range(len(outcols))])
format += " %%%ds" % maxlen
print >> f, "H0 = %.3f +\- %.3f" % (H0, eH0)
print >> f, format % ('#f', 'a', '+/-', 'b', '+/-', 'c', '+/-', 'R', '+/-',
'sig')
for outcol in outcols:
print >> f, format % tuple(outcol)
elif f2 == -1:
# something - B
the_a = -a[:,f1,:]
else:
the_a = a[:,f2,:] - a[:,f1,:]
med_a = median(the_a, axis=0)
covmat = cov(the_a.T)
e_a = sqrt(diag(covmat))
varc = (evar[f1] + evar[f2])/2
this_col = ['%s-%s' % (cols[0],cols[1])]
for i in range(med_a.shape[0]):
this_col += list(sigfig.round_sig_error(med_a[i], e_a[i], 2))
this_col += [sigfig.round_sig(sqrt(varc),2)]
outcols.append(this_col)
format = ""
for j in range(len(outcols[0])):
maxlen = max([len(outcols[i][j]) for i in range(len(outcols))])
format += " %%%ds" % maxlen
labs = ['#clr']
for i in range(med_a.shape[0]):
labs += ['a[%d]' % i,'+/-']
labs += ['sig']
labs = tuple(labs)
print >>f, format % labs